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A boosted chimp optimizer for numerical and engineering design optimization challenges
Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp’s amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minim...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8945882/ https://www.ncbi.nlm.nih.gov/pubmed/35350647 http://dx.doi.org/10.1007/s00366-021-01591-5 |
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author | Kumari, Ch. Leela Kamboj, Vikram Kumar Bath, S. K. Tripathi, Suman Lata Khatri, Megha Sehgal, Shivani |
author_facet | Kumari, Ch. Leela Kamboj, Vikram Kumar Bath, S. K. Tripathi, Suman Lata Khatri, Megha Sehgal, Shivani |
author_sort | Kumari, Ch. Leela |
collection | PubMed |
description | Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp’s amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minima for high-dimensional problems. This research focuses on the improved variants of the chimp optimizer algorithm and named as Boosted chimp optimizer algorithms. In one of the proposed variants, the existing chimp optimizer algorithm has been combined with SHO algorithm to improve the exploration phase of the existing chimp optimizer and named as IChoA-SHO and other variant is proposed to improve the exploitation search capability of the existing ChoA. The testing and validation of the proposed optimizer has been done for various standard benchmarks and Non-convex, Non-linear, and typical engineering design problems. The proposed variants have been evaluated for seven standard uni-modal benchmark functions, six standard multi-modal benchmark functions, ten standard fixed-dimension benchmark functions, and 11 types of multidisciplinary engineering design problems. The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed methods excel than the other existing optimization methods. |
format | Online Article Text |
id | pubmed-8945882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-89458822022-03-25 A boosted chimp optimizer for numerical and engineering design optimization challenges Kumari, Ch. Leela Kamboj, Vikram Kumar Bath, S. K. Tripathi, Suman Lata Khatri, Megha Sehgal, Shivani Eng Comput Original Article Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp’s amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minima for high-dimensional problems. This research focuses on the improved variants of the chimp optimizer algorithm and named as Boosted chimp optimizer algorithms. In one of the proposed variants, the existing chimp optimizer algorithm has been combined with SHO algorithm to improve the exploration phase of the existing chimp optimizer and named as IChoA-SHO and other variant is proposed to improve the exploitation search capability of the existing ChoA. The testing and validation of the proposed optimizer has been done for various standard benchmarks and Non-convex, Non-linear, and typical engineering design problems. The proposed variants have been evaluated for seven standard uni-modal benchmark functions, six standard multi-modal benchmark functions, ten standard fixed-dimension benchmark functions, and 11 types of multidisciplinary engineering design problems. The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed methods excel than the other existing optimization methods. Springer London 2022-03-24 /pmc/articles/PMC8945882/ /pubmed/35350647 http://dx.doi.org/10.1007/s00366-021-01591-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Kumari, Ch. Leela Kamboj, Vikram Kumar Bath, S. K. Tripathi, Suman Lata Khatri, Megha Sehgal, Shivani A boosted chimp optimizer for numerical and engineering design optimization challenges |
title | A boosted chimp optimizer for numerical and engineering design optimization challenges |
title_full | A boosted chimp optimizer for numerical and engineering design optimization challenges |
title_fullStr | A boosted chimp optimizer for numerical and engineering design optimization challenges |
title_full_unstemmed | A boosted chimp optimizer for numerical and engineering design optimization challenges |
title_short | A boosted chimp optimizer for numerical and engineering design optimization challenges |
title_sort | boosted chimp optimizer for numerical and engineering design optimization challenges |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8945882/ https://www.ncbi.nlm.nih.gov/pubmed/35350647 http://dx.doi.org/10.1007/s00366-021-01591-5 |
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